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Integrative multi-omics framework for causal gene discovery in Long COVID

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dc.contributor.authorPinero, S.
dc.contributor.authorLi, X.
dc.contributor.authorLiu, L.
dc.contributor.authorLi, J.
dc.contributor.authorLee, S.H.
dc.contributor.authorWinter, M.
dc.contributor.authorNguyen, T.
dc.contributor.authorZhang, J.
dc.contributor.authorLe, T.D.
dc.contributor.editorJi, B.
dc.date.issued2025
dc.description.abstractLong COVID, or Post-Acute Sequelae of SARS-CoV-2 infection (PASC), affects an estimated 10–20% of COVID-19 patients and presents persistent multisystemic symptoms. Although demographic and clinical factors, such as age, sex, and comorbidities, contribute to risk, the genetic mechanisms underlying this risk remain poorly defined. To address this gap, we developed a multi-omics framework that integrates Transcriptome-Wide Mendelian Randomization (TWMR), Control Theory (CT), Expression Quantitative Trait Loci (eQTL), Genome-Wide Association Studies (GWAS), RNA sequencing (RNA-seq), and Protein-Protein Interaction (PPI) network to identify putative causal genes and network drivers in Long COVID. Our approach prioritized 32 candidate genes, including 19 previously reported and 13 novel, with roles in the SARS-CoV-2 response, viral carcinogenesis, immune regulation, and cell cycle control. Enrichment analyses revealed a shared genetic architecture in syndromic, metabolic, autoimmune, and connective tissue disorders. Using causal gene expression profiles, we identified three distinct symptom-based subtypes of Long COVID, providing information on the heterogeneity of disease mechanisms and clinical presentation. Finally, we developed an open-source Shiny application for interactive exploration of these findings. Together, this integrative framework highlights novel causal mechanisms and therapeutic targets, advancing precision medicine strategies for Long COVID.
dc.description.statementofresponsibilitySindy Pinero, Xiaomei Li, Lin Liu, Jiuyong Li, Sang Hong Lee, Marnie Winter, Thin Nguyen, Junpeng Zhang, Thuc Duy Le
dc.identifier.citationPLoS Computational Biology, 2025; 21(12):e1013725-1-e1013725-32
dc.identifier.doi10.1371/journal.pcbi.1013725
dc.identifier.issn1553-734X
dc.identifier.issn1553-7358
dc.identifier.orcidPinero, S. [0000-0002-6296-6412]
dc.identifier.orcidWinter, M. [0000-0002-7499-789X]
dc.identifier.orcidLe, T.D. [0000-0002-9732-4313]
dc.identifier.urihttps://hdl.handle.net/2440/149309
dc.language.isoen
dc.publisherPublic Library of Science (PLoS)
dc.relation.granthttp://purl.org/au-research/grants/arc/DP230101122
dc.rights© 2025 Pinero et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
dc.source.urihttps://doi.org/10.1371/journal.pcbi.1013725
dc.subjectLong COVID; causal gene discovery
dc.titleIntegrative multi-omics framework for causal gene discovery in Long COVID
dc.typeJournal article
pubs.publication-statusPublished

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